This work proposes MINT (Multimodal Imaging-to-Speech Knowledge Transfer), a three-stage framework that transfers MRI-derived biomarker structure to speech during training and is the first demonstration of MRI-to-speech knowledge transfer for early Alzheimer's screening without imaging at inference.
ShotFinder is introduced, a benchmark that formalizes editing requirements as keyframe-oriented shot descriptions and introduces five types of controllable single-factor constraints: Temporal order, Color, Visual style, Audio, and Resolution.
Tao Yu, Hao-Peng Jin, Hao Wang et al.· arXiv.org· 2 citations
This paper presents a comprehensive overview of the Ultralytics YOLO family, emphasizing architectural evolution, benchmarking, deployment, and emerging directions from YOLOv5 through YOLO27, and examines detection, segmentation, depth, classification, pose, oriented detection, tracking, export, quantization, and deplo...
Deploying multiple machine learning models on resource-constrained robotic platforms for different perception tasks often results in redundant computations, large memory footprints, and complex integration challenges. In response, this work presents Visual Perception Engine (VPEngine), a modular framework designed to e...
Jakub {\L}ucki, Jonathan Becktor, Georgios Georgakis et al.· 0 citations
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Generating semantically controllable 3D CT volumes from radiology reports requires more than a rich text encoder, it requires vision-language alignment grounded in volumetric space. Existing Text-to-CT approaches condition generation on encoders pretrained with language only or 2D vision-language objectives, providing...
Daniele Molino, Camillo Maria Caruso, Filippo Ruffini et al.· 0 citations
This work proposes Aesthetic Alignment: aligning generated images to explicit, user-specified compositional constraints using the Principles of Art (PoA), and introduces CompArt, a dataset of 80,032 WikiArt images augmented with captions and PoA analyses produced by a multimodal LLM under structured prompting.
Zheng-Hao Jin, Tat-Seng Chua· International Conference on...· 1 citation
Urban Land Cover (ULC) classification plays a crucial role in urban planning, environmental monitoring, and sustainable development. We study this task using the ULC dataset from the UCI Machine Learning Repository, which includes tabular features derived from high-resolution aerial imagery across nine classes (e.g., r...
Muntasir Tabasum, Tanpia Tasnim, Md. Ekramul Islam et al.· 0 citations
A dual-stage deep learning framework combining Faster R-CNN for tooth localization with U-Net for pixel-wise caries segmentation in panoramic radiographs is presented, demonstrating superior accuracy compared to existing methods while significantly reducing false-positive rates.
Jihun Kim, Kyeonghun Kim, Jong-yeol Lee et al.· MLMI@MICCAI· 0 citations
Reducing radiation dose in computed tomography significantly degrades image quality and poses challenges for accurate and clinically reliable reconstruction. While recent approaches have shown promise for low-dose CT, they often struggle to generalize across continuous and previously unseen dose levels, leading to arti...
Md Imam Ahasan, Guangchao Yang, A F M Abdun Noor et al.· 0 citations
Robustness controls show that the signal is not explained solely by accompanying OCR and depends on paired visual evidence, and that it extends beyond English.
Girish A. Koushik, Diptesh Kanojia, Helen Treharne· 0 citations
Across four models, attention heads causally necessary for OCR are identified, and it is discovered that these are in fact general-purpose heads that output interpretable semantic features across all image tokens.
Sheridan Feucht, Benno Krojer, Sarah Wang et al.· 0 citations
Generalist-Specialist-MoE (GS-MoE), a two-branch architecture that couples a cross-modal generalist model with distinct modality-specific specialists (experts) via domain-constrained feature fusion, is introduced.
Johannes Kaiser, Florian Braunmiller, D. Rückert et al.· 0 citations
Exploring how generative AI could make machine vision more accessible to businesses. The post GenEye in a Box: Making Machine Vision Something You Can Just Ask For appeared first on GPT-Lab.
Jennifer Neville did not want to go into computer science—but that’s exactly where she landed. Neville discusses the starts and stops that led to her professional sweet spot and her work identifying “surprising failures” making it hard for AI to handle complexity. The post What AI gets wrong and what failure teaches us appeared first on Microsoft Research.
Computer scientist, entrepreneur, and philanthropist will collaborate with the MIT Schwarzman College of Computing to advance AI and scientific discovery.
